Drawing hidden figures of disability: youth and adults with disabilities in Canada
Bibliographic record
Abstract
Background: While governments draw on survey data to inform policy choices, the design, application, and interpretation of surveys can generate certain images of disability and ignore many others. Aims and objectives: This article draws attention to social circumstances of people with disabilities often unacknowledged in research evidence: hidden figures of disability. Methods: Selected results from the Canadian Survey on Disability are examined with a focus on working-age youth and adults (aged 15 to 64) with a range of disabilities. Findings: Five figures of disability and corresponding conceptual models are identified. These hidden figures of disability are the uncounted, those with needs unsupported, youth in multiple transitions, potential workers, and what may be called ‘the fearful’. Several models of disability are identified intersecting with the evidence. These are the absent citizen, biomedical model and charitable model, social and economic integration model, human rights and full citizenship, and psycho-emotional model of affective disablism and ableism. Discussion: Hidden figures of disability are more than statistical tests and texts; more than calculations derived from quantitative research where people become a data point. The function of drawing hidden figures is to disclose and describe the bodily experiences of people with disabilities in their social positions and structural contexts. Conclusion: We need to see the production of evidence for policy not as painting a portrait but as portraits in the plural, and appreciate not only what is in the frame but also what faces and forms of knowledge get glossed over or brushed aside.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".